This paper shows how to make home-helper robots better at long, multi-step chores by smart training on diverse tasks and by polishing the model after training using its own best attempts.
Robots often act like goldfish with short memories; HiF-VLA fixes this by letting them use motion to remember the past and predict the future.
Robots that follow pictures and words (VLA models) can do many tasks, but they often bump into things because safety isnβt guaranteed.